6 citations · 15 across the 23 of their papers we have counts for
6 papers · 1 filter
VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
Kijung Jeon, Thuy-Duong Vuong, Molei Tao
Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider…
A computational phase transition for learning-to-sample from Ising models
Andrej Risteski, Thuy-Duong Vuong
We study \emph{learning-to-sample} -- a basic algorithmic task underlying generative modeling -- for Ising models, a standard testbed for algorithmic ideas in both theoretical comp…
Efficiently learning and sampling multimodal distributions with data-based initialization
Frederic Koehler, Holden Lee, Thuy-Duong Vuong
We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily sl…
Fairness in Submodular Maximization over a Matroid Constraint
Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard +1
Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning. Some of these applications involve decision-making over da…
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization
Frederic Koehler, Thuy-Duong Vuong
There is a long history, as well as a recent explosion of interest, in statistical and generative modeling approaches based on score functions -- derivatives of the log-likelihood…
From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization
Nima Anari, Thuy-Duong Vuong
We show a connection between sampling and optimization on discrete domains. For a family of distributions defined on size subsets of a ground set of elements that is closed…